'''simple docstring'''




import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


snake_case	=      logging.getLogger(__name__)

snake_case	=      tf.data.AUTOTUNE







def       A_ (			):
      _lowerCAmelCase		 = argparse.ArgumentParser(description='Train a masked language model on TPU.'     )
      parser.add_argument(
          '--pretrained_model_config'	,	type=a__	,	default='roberta-base'	,	help='The model config to use. Note that we don\'t copy the model\'s weights, only the config!'	,	)
      parser.add_argument(
          '--tokenizer'	,	type=a__	,	default='unigram-tokenizer-wikitext'	,	help='The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'	,	)

      parser.add_argument(
          '--per_replica_batch_size'	,	type=a__	,	default=8	,	help='Batch size per TPU core.'	,	)

      parser.add_argument(
          '--no_tpu'	,	action='store_true'	,	help='If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'	,	)

      parser.add_argument(
          '--tpu_name'	,	type=a__	,	help='Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'	,	default='local'	,	)

      parser.add_argument(
          '--tpu_zone'	,	type=a__	,	help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'	,	)

      parser.add_argument(
          '--gcp_project'	,	type=a__	,	help='Google cloud project name. Only used for non-Colab TPU nodes.'     )

      parser.add_argument(
          '--bfloat16'	,	action='store_true'	,	help='Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'	,	)

      parser.add_argument(
          '--train_dataset'	,	type=a__	,	help='Path to training dataset to load. If the path begins with `gs://`'
          ' then the dataset will be loaded from a Google Cloud Storage bucket.'	,	)

      parser.add_argument(
          '--shuffle_buffer_size'	,	type=a__	,	default=2**18	,	help='Size of the shuffle buffer (in samples)'	,	)

      parser.add_argument(
          '--eval_dataset'	,	type=a__	,	help='Path to evaluation dataset to load. If the path begins with `gs://`'
          ' then the dataset will be loaded from a Google Cloud Storage bucket.'	,	)

      parser.add_argument(
          '--num_epochs'	,	type=a__	,	default=1	,	help='Number of epochs to train for.'	,	)

      parser.add_argument(
          '--learning_rate'	,	type=a__	,	default=1e-4	,	help='Learning rate to use for training.'	,	)

      parser.add_argument(
          '--weight_decay_rate'	,	type=a__	,	default=1e-3	,	help='Weight decay rate to use for training.'	,	)

      parser.add_argument(
          '--max_length'	,	type=a__	,	default=512	,	help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'	,	)

      parser.add_argument(
          '--mlm_probability'	,	type=a__	,	default=0.15	,	help='Fraction of tokens to mask during training.'	,	)

      parser.add_argument('--output_dir'	,	type=a__	,	required=a__	,	help='Path to save model checkpoints to.'     )
      parser.add_argument('--hub_model_id'	,	type=a__	,	help='Model ID to upload to on the Hugging Face Hub.'     )

      _lowerCAmelCase		 = parser.parse_args()
      return args







def       A_ (			_lowerCamelCase      :				Optional[Any]     ):






      try:
            if args.tpu_name:
                  _lowerCAmelCase		 = tf.distribute.cluster_resolver.TPUClusterResolver(
                      args.tpu_name	,	zone=args.tpu_zone	,	project=args.gcp_project     )
            else:
                  _lowerCAmelCase		 = tf.distribute.cluster_resolver.TPUClusterResolver()
      except ValueError:
            raise RuntimeError(
                'Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '
                '--gcp_project. When running on a TPU VM, use --tpu_name local.'     )

      tf.config.experimental_connect_to_cluster(a__     )
      tf.tpu.experimental.initialize_tpu_system(a__     )

      return tpu







def       A_ (			_lowerCamelCase      :				str     ):
      _lowerCAmelCase		 = 0
      for file in file_list:
            _lowerCAmelCase		 = file.split('/'     )[-1]
            _lowerCAmelCase		 = re.search(r'-\d+-(\d+)\.tfrecord'	,	a__     ).group(1     )
            _lowerCAmelCase		 = int(a__     )
            num_samples += sample_count

      return num_samples







def       A_ (			_lowerCamelCase      :				Any	,	_lowerCamelCase      :				Dict	,	_lowerCamelCase      :				List[str]	,	_lowerCamelCase      :				List[str]	,	_lowerCamelCase      :				Dict	,	_lowerCamelCase      :				List[Any]=None     ):
      _lowerCAmelCase		 = count_samples(a__     )
      _lowerCAmelCase		 = tf.data.Dataset.from_tensor_slices(a__     )
      if shuffle:
            _lowerCAmelCase		 = dataset.shuffle(len(a__     )     )
      _lowerCAmelCase		 = tf.data.TFRecordDataset(a__	,	num_parallel_reads=a__     )
      # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
      _lowerCAmelCase		 = dataset.apply(tf.data.experimental.assert_cardinality(a__     )     )
      _lowerCAmelCase		 = dataset.map(a__	,	num_parallel_calls=a__     )
      if shuffle:
            assert shuffle_buffer_size is not None
            _lowerCAmelCase		 = dataset.shuffle(args.shuffle_buffer_size     )
      _lowerCAmelCase		 = dataset.batch(a__	,	drop_remainder=a__     )
      _lowerCAmelCase		 = dataset.map(a__	,	num_parallel_calls=a__     )
      _lowerCAmelCase		 = dataset.prefetch(a__     )
      return dataset







def       A_ (			_lowerCamelCase      :				List[str]     ):






      if not args.no_tpu:
            _lowerCAmelCase		 = initialize_tpu(a__     )
            _lowerCAmelCase		 = tf.distribute.TPUStrategy(a__     )
      else:
            _lowerCAmelCase		 = tf.distribute.OneDeviceStrategy(device='/gpu:0'     )

      if args.bfloataa:
            tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'     )

      _lowerCAmelCase		 = AutoTokenizer.from_pretrained(args.tokenizer     )
      _lowerCAmelCase		 = AutoConfig.from_pretrained(args.pretrained_model_config     )
      _lowerCAmelCase		 = tokenizer.vocab_size

      _lowerCAmelCase		 = tf.io.gfile.glob(os.path.join(args.train_dataset	,	'*.tfrecord'     )     )
      if not training_records:
            raise ValueError(F"No .tfrecord files found in {args.train_dataset}."     )
      _lowerCAmelCase		 = tf.io.gfile.glob(os.path.join(args.eval_dataset	,	'*.tfrecord'     )     )
      if not eval_records:
            raise ValueError(F"No .tfrecord files found in {args.eval_dataset}."     )

      _lowerCAmelCase		 = count_samples(a__     )

      _lowerCAmelCase		 = num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
      _lowerCAmelCase		 = steps_per_epoch * args.num_epochs

      with strategy.scope():
            _lowerCAmelCase		 = TFAutoModelForMaskedLM.from_config(a__     )
            model(model.dummy_inputs     )  # Pass some dummy inputs through the model to ensure all the weights are built
            _lowerCAmelCase		 = create_optimizer(
                num_train_steps=a__	,	num_warmup_steps=total_train_steps // 20	,	init_lr=args.learning_rate	,	weight_decay_rate=args.weight_decay_rate	,	)

            # Transformers models compute the right loss for their task by default when labels are passed, and will
            # use this for training unless you specify your own loss function in compile().
            model.compile(optimizer=a__	,	metrics=['accuracy']     )

      def decode_fn(_lowerCamelCase      :				Union[str, Any]     ):
            _lowerCAmelCase		 = {
                'input_ids': tf.io.FixedLenFeature(dtype=tf.intaa	,	shape=(args.max_length,)     ),
                'attention_mask': tf.io.FixedLenFeature(dtype=tf.intaa	,	shape=(args.max_length,)     ),
            }
            return tf.io.parse_single_example(a__	,	a__     )

      # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
      # use their methods in our data pipeline.
      _lowerCAmelCase		 = DataCollatorForLanguageModeling(
          tokenizer=a__	,	mlm_probability=args.mlm_probability	,	mlm=a__	,	return_tensors='tf'     )

      def mask_with_collator(_lowerCamelCase      :				Any     ):
            # TF really needs an isin() function
            _lowerCAmelCase		 = (
                ~tf.cast(batch['attention_mask']	,	tf.bool     )
                | (batch['input_ids'] == tokenizer.cls_token_id)
                | (batch['input_ids'] == tokenizer.sep_token_id)
            )
            _lowerCAmelCase		 = data_collator.tf_mask_tokens(
                batch['input_ids']	,	vocab_size=len(a__     )	,	mask_token_id=tokenizer.mask_token_id	,	special_tokens_mask=a__	,	)
            return batch

      _lowerCAmelCase		 = args.per_replica_batch_size * strategy.num_replicas_in_sync

      _lowerCAmelCase		 = prepare_dataset(
          a__	,	decode_fn=a__	,	mask_fn=a__	,	batch_size=a__	,	shuffle=a__	,	shuffle_buffer_size=args.shuffle_buffer_size	,	)

      _lowerCAmelCase		 = prepare_dataset(
          a__	,	decode_fn=a__	,	mask_fn=a__	,	batch_size=a__	,	shuffle=a__	,	)

      _lowerCAmelCase		 = []
      if args.hub_model_id:
            callbacks.append(
                PushToHubCallback(output_dir=args.output_dir	,	hub_model_id=args.hub_model_id	,	tokenizer=a__     )     )

      model.fit(
          a__	,	validation_data=a__	,	epochs=args.num_epochs	,	callbacks=a__	,	)

      model.save_pretrained(args.output_dir     )


if __name__ == "__main__":
 snake_case	=      parse_args()
 main(args)













"""simple docstring"""


from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						for param, grad_param in zip(model_a.parameters()     ,      model_b.parameters() ):
												if not param.requires_grad:
																		continue
												if not did_step:
																		# Grads should not be in sync
																		assert (
																		    torch.allclose(param.grad     ,      grad_param.grad ) is False
																		), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
												else:
																		# Grads should be in sync
																		assert (
																		    torch.allclose(param.grad     ,      grad_param.grad ) is True
																		), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__     ,      a__=True ):





						'''simple docstring'''






						model.train()
						lowerCAmelCase   :Dict						=			model(a__ )
						lowerCAmelCase   :Union[str, Any]						=			F.mse_loss(a__     ,      target.to(output.device ) )
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(a__ )


def        UpperCAmelCase      (					a__     ,      a__=False ):





						'''simple docstring'''






						set_seed(42 )
						lowerCAmelCase   :Any						=			RegressionModel()
						lowerCAmelCase   :List[str]						=			deepcopy(a__ )
						lowerCAmelCase   :str						=			RegressionDataset(length=80 )
						lowerCAmelCase   :Optional[Any]						=			DataLoader(a__     ,      batch_size=16 )
						model.to(accelerator.device )
						if sched:
												lowerCAmelCase   :List[Any]						=			AdamW(params=model.parameters()     ,      lr=1e-3 )
												lowerCAmelCase   :Dict						=			AdamW(params=ddp_model.parameters()     ,      lr=1e-3 )
												lowerCAmelCase   :Tuple						=			LambdaLR(a__     ,      lr_lambda=lambda a__ : epoch**0.65 )
												lowerCAmelCase   :Optional[Any]						=			LambdaLR(a__     ,      lr_lambda=lambda a__ : epoch**0.65 )
						# Make a copy of `model`
						if sched:
												lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :int						=			accelerator.prepare(a__     ,      a__     ,      a__     ,      a__ )
						else:
												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			accelerator.prepare(a__     ,      a__ )
						if sched:
												return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
						return model, ddp_model, dataloader


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Any						=			get_training_setup(a__ )
						# Use a single batch
						lowerCAmelCase	, lowerCAmelCase   :Any						=			next(iter(a__ ) ).values()
						for iteration in range(3 ):
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(a__ ):
																								step_model(a__     ,      a__     ,      a__     ,      a__ )
												else:
																		# Sync grads
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
												check_model_parameters(a__     ,      a__     ,      a__     ,      a__ )
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		assert torch.allclose(
																		    param.grad     ,      ddp_param.grad ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

												# Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :Any						=			ddp_input[torch.randperm(len(a__ ) )]


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Dict						=			get_training_setup(a__ )
						# Use a single batch
						lowerCAmelCase	, lowerCAmelCase   :str						=			next(iter(a__ ) ).values()
						for iteration in range(3 ):
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :int						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(a__ ):
																								step_model(a__     ,      a__     ,      a__     ,      a__ )
												else:
																		# Sync grads
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		if iteration % 2 == 0:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is False
																								), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
																		else:
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is True
																								), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :str						=			ddp_input[torch.randperm(len(a__ ) )]


def        UpperCAmelCase      (					a__=False     ,      a__=False ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			Accelerator(
						    split_batches=a__     ,      dispatch_batches=a__     ,      gradient_accumulation_steps=2 )
						# Test that context manager behaves properly
						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			get_training_setup(a__ )
						for iteration, batch in enumerate(a__ ):
												lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			batch.values()
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Dict						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Any						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												with accelerator.accumulate(a__ ):
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		if ((iteration + 1) % 2 == 0) or (iteration == len(a__ ) - 1):
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is True
																								), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
																		else:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is False
																								), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :Optional[int]						=			ddp_input[torch.randperm(len(a__ ) )]
						GradientState._reset_state()


def        UpperCAmelCase      (					a__=False     ,      a__=False ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[int]						=			Accelerator(
						    split_batches=a__     ,      dispatch_batches=a__     ,      gradient_accumulation_steps=2 )
						# Test that context manager behaves properly
						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			get_training_setup(a__     ,      a__ )
						for iteration, batch in enumerate(a__ ):
												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			batch.values()
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												model.train()
												ddp_model.train()
												step_model(a__     ,      a__     ,      a__     ,      a__     ,      a__ )
												opt.step()

												if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(a__ )):
																		if split_batches:
																								sched.step()
																		else:
																								for _ in range(accelerator.num_processes ):
																														sched.step()
												opt.zero_grad()
												# Perform gradient accumulation under wrapper
												with accelerator.accumulate(a__ ):
																		step_model(a__     ,      a__     ,      a__     ,      a__ )
																		ddp_opt.step()
																		ddp_sched.step()
																		ddp_opt.zero_grad()

												# Learning rates should be the same
												assert (
												    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
												), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
												lowerCAmelCase   :int						=			(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(a__ ))
												if accelerator.num_processes > 1:
																		check_model_parameters(a__     ,      a__     ,      a__     ,      a__ )
												# Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
						GradientState._reset_state()


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :int						=			Accelerator()

						lowerCAmelCase   :int						=			RegressionDataset(length=80 )
						lowerCAmelCase   :Optional[Any]						=			DataLoader(a__     ,      batch_size=16 )
						lowerCAmelCase   :Any						=			RegressionDataset(length=96 )
						lowerCAmelCase   :Dict						=			DataLoader(a__     ,      batch_size=16 )
						lowerCAmelCase	, lowerCAmelCase   :Tuple						=			accelerator.prepare(a__     ,      a__ )
						assert accelerator.gradient_state.active_dataloader is None
						for iteration, _ in enumerate(a__ ):
												assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
												if iteration < len(a__ ) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
																		if iteration == 1:
																								for batch_num, _ in enumerate(a__ ):
																														assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
																														if batch_num < len(a__ ) - 1:
																																				assert not accelerator.gradient_state.end_of_dataloader
																														else:
																																				assert accelerator.gradient_state.end_of_dataloader
												else:
																		assert accelerator.gradient_state.end_of_dataloader
						assert accelerator.gradient_state.active_dataloader is None


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :List[str]						=			Accelerator()
						lowerCAmelCase   :Optional[int]						=			accelerator.state
						if state.local_process_index == 0:
												print('**Test `accumulate` gradient accumulation with dataloader break**' )
						test_dataloader_break()
						if state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print('**Test NOOP `no_sync` context manager**' )
												test_noop_sync(a__ )
						if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
												if state.local_process_index == 0:
																		print('**Test Distributed `no_sync` context manager**' )
												test_distributed_sync(a__ )
						if state.distributed_type == DistributedType.MULTI_GPU:
												for split_batch in [True, False]:
																		for dispatch_batches in [True, False]:
																								if state.local_process_index == 0:
																														print(
																														    '**Test `accumulate` gradient accumulation, '     ,      F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""     ,      )
																								test_gradient_accumulation(a__     ,      a__ )

    # Currently will break on torch 2.0 +, need to investigate why
						if is_torch_version('<'     ,      '2.0' ) or state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print(
																		    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,      '`split_batches=False`, `dispatch_batches=False`**'     ,      )
												test_gradient_accumulation_with_opt_and_scheduler()
												if state.distributed_type == DistributedType.MULTI_GPU:
																		for split_batch in [True, False]:
																								for dispatch_batches in [True, False]:
																														if not split_batch and not dispatch_batches:
																																				continue
																														if state.local_process_index == 0:
																																				print(
																																				    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,      F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""     ,      )
																														test_gradient_accumulation_with_opt_and_scheduler(a__     ,      a__ )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						main()


if __name__ == "__main__":
		main()